CN107818199A - Mountain area 10kV line-to-ground resistive arrangement methods based on adaptive neural network - Google Patents

Mountain area 10kV line-to-ground resistive arrangement methods based on adaptive neural network Download PDF

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CN107818199A
CN107818199A CN201710888308.8A CN201710888308A CN107818199A CN 107818199 A CN107818199 A CN 107818199A CN 201710888308 A CN201710888308 A CN 201710888308A CN 107818199 A CN107818199 A CN 107818199A
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mrow
msub
sampling instant
formula
munderover
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叶铁丰
郑明�
陈小波
何顺勇
陈伟
陈雷
戴志博
龙翔
杨凡
潘锡杰
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State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
Wenzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Ruian Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
Wenzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Ruian Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Priority to CN201710888308.8A priority Critical patent/CN107818199A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/30—Circuit design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

The present invention discloses a kind of mountain area 10kV line-to-ground resistive arrangement methods based on adaptive neural network.Measured using soil resistivity table and obtain the stratified soil thickness in region residing for 10kV overhead line structures and the multiple sample datas of layered resistance rate, input signal using sample data as adaptive neural network, using equivalent soil resistivity as output signal, connection weight between adaptive neural network input layer, hidden layer and output layer is optimized by swarm optimization method, equivalent soil resistivity optimal value is obtained after iterative learning, then the grounding resistance after being optimized by grounding resistance design formula.Using the accurate calculating of 10kV overhead transmission line shaft tower grounding resistances under the achievable mountain area complicated geological environment of the present invention, accurately design and manufacture bases are provided for 10kV overhead transmission line lightening protection engineerings.With the advantages below not available for prior art：Computational accuracy is higher, and computational methods Project Realization is simpler.

Description

Mountain area 10kV line-to-ground resistive arrangement methods based on adaptive neural network
Technical field
The present invention relates to the grounding technology of field of power, more particularly to a kind of mountain area based on adaptive neural network 10kV line-to-ground resistive arrangement methods.
Background technology
Grounding Technology of Modern Power System is to ensure power system and its pass of electrical equipment safe and reliable operation and personal security One of key technology.At present, the instrument of most measurement soil resistivities can not measure the soil electricity of different depth level simultaneously Resistance rate, can not monitor the soil resistivity value of multiple spot incessantly for a long time, thus project planner be difficult to it is straight by instrument The mode for connecing measurement obtains the real soil resistivity value of complicated geological environment.At present, industry is mainly based upon power system and connect The related state's network planning journey in ground, numerical computations, main flow are carried out to the ground connection parameter of earthed system with reference to Computer-aided Design Technology Method include FInite Element, compound image method, CDEGS (Current Distribution, Electromagnetic Fields, Grounding and Soil Structure Analysis) software analysis method, but because current field is distributed in nothing In the big complicated soil space of limit, have for the grounding resistance optimization design under complicated geological environment extremely challenging.
Neutral net has the advantages that stronger non-linear mapping capability, generalization ability and preferable fault-tolerance, therefore It is widely used, but is being grounded in fields such as pattern-recognition, System Discrimination, image procossing, optimization calculating, optimum controls Also rarely has application in resistance optimization design.On the other hand, the parameter to be optimized adjusted is more in neutral net, a large amount of connection weights The design of parameter relies primarily on the design experiences and test of many times of designer at present, thus how to design one towards specific work Journey item purpose adaptive neural network is as one of domestic and international academia and engineer applied field problem urgently to be resolved hurrily.
The content of the invention
In view of the above-mentioned deficiencies in the prior art, it is an object of the present invention to provide the mountain area 10kV lines based on adaptive neural network Road grounding resistance design method.
In order to solve the above-mentioned technical problem, the present invention is achieved by the following technical solutions：Based on adaptive neural network net The mountain area 10kV line-to-ground resistive arrangement methods of network, this method comprise the following steps：
A, for 10kV overhead transmission lines to be designed residing for geological environment, using soil resistivity table measure obtain the region The sample data of diverse location, including upper layer of soil thickness tu, upper layer of soil electricalresistivityρu, BC soil thickness tm, BC soil ElectricalresistivityρmWith lower soil electricalresistivityρl, wherein N is the maximal dimension of sample；
Adaptive neural network parameter values, including input layer number n are set B,I, output layer nodes nO, hidden layer NodesRepresent (nI+nO)0.5Round up number, population scale M, maximum Iterations Imax, crossover probability pc, mutation probability pm；
C, the initialization population P of real coding is generated at random0={ Sh, h=1,2 ..., M }, wherein Sh=(wij(1),wjl (1)), i=1,2 ..., nI, j=1,2 ..., nH, l=1,2 ..., nO, wij(1) the 1st sampling instant input node i is represented With the connection weight between hidden layer node j, wjl(1) represent the 1st sampling instant hidden layer node j and output node layer l it Between connection weight；
D, P in assessing0The fitness of each individual；
E, by { F (Sh), h=1,2 .., M } sorted according to ascending power, by the minimum individual mark of fitness value for it is current most Good individual Sbest, by the fitness value of minimum labeled as current preferably target function value Fbest；
F, according to the cumulative probability shown in formula (8) from P0Selection produces new colony P；
Wherein, p (Sh) represent individual ShCumulative probability；
G, according to crossover probability pcTwo individual S are randomly choosed out from PiAnd Si+1, intersect according to formula (9) and produce new two Individual SniAnd Sn(i+1), SniAnd Sn(i+1)Original S is replaced respectivelyiAnd Si+1, other individuals keep constant in P, new by now Population is labeled as Pc={ Sch, h=1,2 ..., M }, wherein SchRepresent PcIn h-th individual；
Caused random number between wherein α represents 0 to 1；
H, according to mutation probability pmFrom PcThe middle individual S of selectionch, according to formula (10) to SciRandom variation is carried out, is produced new Individual SNh, and by SNhInstead of Sch, keep PcIn other individuals it is constant, so as to produce new population PN；
SNh=Sch+(r-0.5)×max{2(Sch-L),2(U-Sch)} (10)
Wherein L and U represent S respectivelychLower and upper limit, r represent 0 to 1 between caused random number；
I, P is unconditionally received0=PN；
J, repeat step D to step I is until meeting largest optimization iterations Imax；
K, the current preferably target function value F of outputbestWith best individual Sbest, and equivalent soil resistivity corresponding to output Predicted value ρ=yOl(Tmax)；
L, 10kV overhead transmission line shaft tower grounding resistances R is calculated according to formula (11)；
Wherein, ρ is the soil resistivity obtained in step (11), and L is grounded for 10kV overhead transmission lines Reinforced Concrete Pole Tower The total length of pole, h be earthing pole the depth of burying, d be earthing pole diameter, AtTo be grounded the form factor of level.
Preferably, the step D assesses P in accordance with the following steps0In each individual fitness：
D1, make k=1；
D2, by tu、ρu、tm、ρmAnd ρlAs the input signal of adaptive neural network, by the input of kth time sampling instant Signal XI(k) it is defined as XI(k)={ xi(k), i=1,2 ..., nI, wherein nIFor neural network input layer nodes, in this nI =5, x1(k)=tu,x2(k)=ρu,x3(k)=tm,x4(k)=ρm,x5(k)=ρl；
D3, the neutral net hidden layer input signal X for calculating kth time sampling instantHI(k)={ xHIj(k), j=1, 2,...,nH, wherein xHIj(k) calculate as shown in formula (1)：
Wherein, wij(k) connection weight of the kth time between sampling instant input node i and hidden layer node j is represented；
D4, the neutral net hidden layer output signal X for calculating kth time sampling instantHO(k)={ xHOj(k), j=1, 2,...,nH, wherein xHjO(k) calculate as shown in formula (2)：
D5, the neutral net output layer signal Y for calculating kth time sampling instantO(k)={ yOl(k), l=1,2 ..., nO, Wherein nORepresent output layer number of nodes, yOl(k) calculate as shown in formula (3)：
Wherein, wjl(k) connection weight between kth time sampling instant hidden layer node j and output node layer l is represented；
D6, assess according to formula (4) kth time sampling instant neutral net error performance index J (k)；
Wherein, yOlr(k) desired value of the equivalent soil resistivity of kth time sampling instant is represented；
D7, the weight coefficient w according to formula (5) renewal+1 sampling instant of kthij(k+1)；
Wherein, η represents learning rate, and β represents factor of momentum；
D8, the weight coefficient w according to formula (6) renewal+1 sampling instant of kthjl(k+1), and k=k+1 is made；
wjl(k+1)=wjl(k)+η(yOlr(k)-yOl(k))xHOj(k)+β(wij(k)-wij(k-1)) (6)
D9, repeat step D2 are to step D8 until meeting k=Tmax, wherein TmaxRepresent maximum sampling number；
D10, according to formula (7) calculate individual ShFitness F (Sh)；
Compared with prior art, it is an advantage of the invention that：Using the achievable mountain area complicated geological environment 10kV framves of the present invention The accurate calculating of ceases to be busy line pole tower grounding resistance, accurately design and manufacture bases are provided for 10kV overhead transmission line lightening protection engineerings. With the advantages below not available for prior art：Computational accuracy is higher, and computational methods Project Realization is simpler.
Brief description of the drawings
Fig. 1 is the mountain area 10kV line-to-ground resistive arrangement Method And Principle schematic diagrames based on adaptive neural network.
Embodiment
Embodiments of the invention are described below in detail, the example of the embodiment is shown in the drawings.Below with reference to The embodiment of accompanying drawing description is exemplary, it is intended to for explaining the present invention, and is not considered as limiting the invention.
Refering to the implementation that Fig. 1 is the mountain area 10kV line-to-ground resistive arrangement methods of the invention based on adaptive neural network Example, by taking Zhejiang Nanshan District 10kV overhead transmission lines as an example, using the mountain area 10kV proposed by the present invention based on adaptive neural network Line-to-ground resistive arrangement method is implemented.
A, for 10kV overhead transmission lines to be designed residing for geological environment, obtained using ES3010E soil resistivity table measurements Obtain the sample data of the region diverse location, including upper layer of soil thickness tu, upper layer of soil electricalresistivityρu, BC soil thickness tm、 BC soil electricalresistivityρmWith lower soil electricalresistivityρl, wherein N is the maximal dimension of sample；
Adaptive neural network parameter values, including input layer number n are set B,I=5, output layer nodes nO=1, Node in hidden layer, population scale M=30, maximum iteration Imax=50, crossover probability pc =0.8, mutation probability pm=0.1；
C, the initialization population P of real coding is generated at random0={ Sh, h=1,2 ..., M }, wherein Sh=(wij(1),wjl (1)), i=1,2 ..., 5, j=1,2 ..., 8, l=1, wij(1) the 1st sampling instant input node i and hidden layer section are represented Connection weight between point j, wjl(1) represent the 1st sampling instant hidden layer node j and export the connection weight between node layer l Weight；
D, P is assessed in accordance with the following steps0In each individual fitness；
D1, make k=1；
D2, by tu、ρu、tm、ρmAnd ρlAs the input signal of adaptive neural network, by the input of kth time sampling instant Signal XI(k) it is defined as XI(k)={ xi(k), i=1,2 ..., nI, wherein nIFor neural network input layer nodes, in this nI =5, x1(k)=tu,x2(k)=ρu,x3(k)=tm,x4(k)=ρm,x5(k)=ρl；
D3, the neutral net hidden layer input signal X for calculating kth time sampling instantHI(k)={ xHIj(k), j=1, 2,...,nH, wherein xHIj(k) calculate as shown in formula (1)：
Wherein, wij(k) connection weight of the kth time between sampling instant input node i and hidden layer node j is represented；
D4, the neutral net hidden layer output signal X for calculating kth time sampling instantHO(k)={ xHOj(k), j=1, 2,...,nH, wherein xHjO(k) calculate as shown in formula (2)：
D5, the neutral net output layer signal Y for calculating kth time sampling instantO(k)={ yOl(k), l=1,2 ..., nO, Wherein nORepresent output layer number of nodes, yOl(k) calculate as shown in formula (3)：
Wherein, wjl(k) connection weight between kth time sampling instant hidden layer node j and output node layer l is represented；
D6, assess according to formula (4) kth time sampling instant neutral net error performance index J (k)；
Wherein, yOlr(k) desired value of the equivalent soil resistivity of kth time sampling instant is represented；
D7, the weight coefficient w according to formula (5) renewal+1 sampling instant of kthij(k+1)；
Wherein, η represents learning rate, and β represents factor of momentum；
D8, the weight coefficient w according to formula (6) renewal+1 sampling instant of kthjl(k+1), and k=k+1 is made；
wjl(k+1)=wjl(k)+η(yOlr(k)-yOl(k))xHOj(k)+β(wij(k)-wij(k-1)) (6)
D9, repeat step D2 are to step D8 until meeting k=Tmax, wherein TmaxRepresent maximum sampling number；
D10, according to formula (7) calculate individual ShFitness F (Sh)；
E, by { F (Sh), h=1,2 .., M } sorted according to ascending power, by the minimum individual mark of fitness value for it is current most Good individual Sbest, by the fitness value of minimum labeled as current preferably target function value Fbest；
F, according to the cumulative probability shown in formula (8) from P0Selection produces new colony P；
Wherein, p (Sh) represent individual ShCumulative probability；
G, according to crossover probability pcTwo individual S are randomly choosed out from PiAnd Si+1, intersect according to formula (9) and produce new two Individual SniAnd Sn(i+1), SniAnd Sn(i+1)Original S is replaced respectivelyiAnd Si+1, other individuals keep constant in P, new by now Population is labeled as Pc={ Sch, h=1,2 ..., M }, wherein SchRepresent PcIn h-th individual；
Caused random number between wherein α represents 0 to 1；
H, according to mutation probability pmFrom PcThe middle individual S of selectionch, according to formula (10) to SciRandom variation is carried out, is produced new Individual SNh, and by SNhInstead of Sch, keep PcIn other individuals it is constant, so as to produce new population PN；
SNh=Sch+(r-0.5)×max{2(Sch-L),2(U-Sch)} (10)
Wherein L and U represent S respectivelychLower and upper limit, r represent 0 to 1 between caused random number；
I, P is unconditionally received0=PN；
J, repeat step (4) to step (9) until meeting largest optimization iterations Imax=50；
K, the current preferably target function value F of outputbestWith best individual Sbest, and equivalent soil resistivity corresponding to output Predicted value ρ=yOl(Tmax)；
L, 10kV overhead transmission line shaft tower grounding resistances R is calculated according to formula (11)；
Wherein, ρ is the soil resistivity obtained in step (11), and L is grounded for 10kV overhead transmission lines Reinforced Concrete Pole Tower The total length of pole, h be earthing pole the depth of burying, d be earthing pole diameter, AtTo be grounded the form factor of level, in of the invention Used Radiation coefficient is At=2.
By to the experimental result pair using the prior art such as the technology of the present invention and FInite Element, compound image method, CDEGS Than analysis, we can be found that：Calculated using the mountain area complicated geological environment 10kV overhead transmission line shaft towers earth resistance meter of the present invention Precision compared with prior art at least improve more than 2%, for 10kV overhead transmission line lightening protection engineerings provides accurately design and construction according to According to.With the advantages below not available for prior art：Computational accuracy is higher, and computational methods Project Realization is simpler.
The specific embodiment of the present invention is the foregoing is only, but the technical characteristic of the present invention is not limited thereto, Ren Heben The technical staff in field in the field of the invention, all cover among the scope of the claims of the present invention by the change or modification made.

Claims (2)

1. the mountain area 10kV line-to-ground resistive arrangement methods based on adaptive neural network, it is characterised in that：This method includes Following steps：
A, for 10kV overhead transmission lines to be designed residing for geological environment, measure that to obtain the region different using soil resistivity table The sample data of position, including upper layer of soil thickness tu, upper layer of soil electricalresistivityρu, BC soil thickness tm, BC soil resistance Rate ρmWith lower soil electricalresistivityρl, wherein N is the maximal dimension of sample；
Adaptive neural network parameter values, including input layer number n are set B,I, output layer nodes nO, hidden layer node Number Represent (nI+nO)0.5Round up number, population scale M, greatest iteration time Number Imax, crossover probability pc, mutation probability pm；
C, the initialization population P of real coding is generated at random0={ Sh, h=1,2 ..., M }, wherein Sh=(wij(1),wjl(1)), I=1,2 ..., nI, j=1,2 ..., nH, l=1,2 ..., nO, wij(1) represent the 1st sampling instant input node i with it is hidden Connection weight between j containing node layer, wjl(1) represent between the 1st sampling instant hidden layer node j and output node layer l Connection weight；
D, P in assessing0The fitness of each individual；
E, by { F (Sh), h=1,2 .., M } sorted according to ascending power, it is current preferably individual by the minimum individual mark of fitness value Sbest, by the fitness value of minimum labeled as current preferably target function value Fbest；
F, according to the cumulative probability shown in formula (8) from P0Selection produces new colony P；
<mrow> <mi>p</mi> <mrow> <mo>(</mo> <msub> <mi>S</mi> <mi>h</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>h</mi> </munderover> <mi>F</mi> <mrow> <mo>(</mo> <msub> <mi>S</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <mi>F</mi> <mrow> <mo>(</mo> <msub> <mi>S</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>,</mo> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>M</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow>
Wherein, p (Sh) represent individual ShCumulative probability；
G, according to crossover probability pcTwo individual S are randomly choosed out from PiAnd Si+1, intersect according to formula (9) and produce new two Body SniAnd Sn(i+1), SniAnd Sn(i+1)Original S is replaced respectivelyiAnd Si+1, other individuals keep constant in P, by new population now Labeled as Pc={ Sch, h=1,2 ..., M }, wherein SchRepresent PcIn h-th individual；
<mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mi>S</mi> <mrow> <mi>n</mi> <mi>i</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;alpha;S</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>&amp;alpha;</mi> <mo>)</mo> </mrow> <msub> <mi>S</mi> <mi>i</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>S</mi> <mrow> <mi>n</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;alpha;S</mi> <mi>i</mi> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>&amp;alpha;</mi> <mo>)</mo> </mrow> <msub> <mi>S</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow>
Caused random number between wherein α represents 0 to 1；
H, according to mutation probability pmFrom PcThe middle individual S of selectionch, according to formula (10) to SciRandom variation is carried out, produces new Body SNh, and by SNhInstead of Sch, keep PcIn other individuals it is constant, so as to produce new population PN；
SNh=Sch+(r-0.5)×max{2(Sch-L),2(U-Sch)} (10)
Wherein L and U represent S respectivelychLower and upper limit, r represent 0 to 1 between caused random number；
I, P is unconditionally received0=PN；
J, repeat step D to step I is until meeting largest optimization iterations Imax；
K, the current preferably target function value F of outputbestWith best individual Sbest, and equivalent soil resistivity corresponding to output is pre- Measured value ρ=yOl(Tmax)；
L, 10kV overhead transmission line shaft tower grounding resistances R is calculated according to formula (11)；
<mrow> <mi>R</mi> <mo>=</mo> <mfrac> <mi>&amp;rho;</mi> <mrow> <mn>2</mn> <mi>&amp;pi;</mi> <mi>L</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <mi>l</mi> <mi>n</mi> <mfrac> <msup> <mi>L</mi> <mn>2</mn> </msup> <mrow> <mi>h</mi> <mi>d</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>A</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow>
Wherein, ρ is the soil resistivity obtained in step (11), and L is 10kV overhead transmission line Reinforced Concrete Pole Tower earthing poles Total length, h be earthing pole the depth of burying, d be earthing pole diameter, AtTo be grounded the form factor of level.
2. the mountain area 10kV line-to-ground resistive arrangement methods based on adaptive neural network as claimed in claim 1, it is special Sign is：The step D assesses P in accordance with the following steps0In each individual fitness：
D1, make k=1；
D2, by tu、ρu、tm、ρmAnd ρlAs the input signal of adaptive neural network, by the input signal of kth time sampling instant XI(k) it is defined as XI(k)={ xi(k), i=1,2 ..., nI, wherein nIFor neural network input layer nodes, in this nI=5, x1(k)=tu,x2(k)=ρu,x3(k)=tm,x4(k)=ρm,x5(k)=ρl；
D3, the neutral net hidden layer input signal X for calculating kth time sampling instantHI(k)={ xHIj(k), j=1,2 ..., nH, wherein xHIj(k) calculate as shown in formula (1)：
<mrow> <msub> <mi>x</mi> <mrow> <mi>H</mi> <mi>I</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>n</mi> <mi>I</mi> </msub> </munderover> <msub> <mi>w</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <msub> <mi>x</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>n</mi> <mi>H</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
Wherein, wij(k) connection weight of the kth time between sampling instant input node i and hidden layer node j is represented；D4, calculate the The neutral net hidden layer output signal X of k sampling instantHO(k)={ xHOj(k), j=1,2 ..., nH, wherein xHjO(k) count Calculate as shown in formula (2)：
<mrow> <msub> <mi>x</mi> <mrow> <mi>H</mi> <mi>O</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mn>1</mn> <mo>+</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msub> <mi>x</mi> <mrow> <mi>H</mi> <mi>I</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </mrow> </msup> </mrow> </mfrac> <mo>,</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>n</mi> <mi>H</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
D5, the neutral net output layer signal Y for calculating kth time sampling instantO(k)={ yOl(k), l=1,2 ..., nO, wherein nORepresent output layer number of nodes, yOl(k) calculate as shown in formula (3)：
<mrow> <msub> <mi>y</mi> <mrow> <mi>O</mi> <mi>l</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>n</mi> <mi>H</mi> </msub> </munderover> <msub> <mi>w</mi> <mrow> <mi>j</mi> <mi>l</mi> </mrow> </msub> <msub> <mi>x</mi> <mrow> <mi>H</mi> <mi>O</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>l</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>n</mi> <mi>O</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>
Wherein, wjl(k) connection weight between kth time sampling instant hidden layer node j and output node layer l is represented；
D6, assess according to formula (4) kth time sampling instant neutral net error performance index J (k)；
Wherein, yOlr(k) desired value of the equivalent soil resistivity of kth time sampling instant is represented；
D7, the weight coefficient w according to formula (5) renewal+1 sampling instant of kthij(k+1)；
<mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mi>w</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>w</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>&amp;eta;</mi> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>n</mi> <mi>O</mi> </msub> </munderover> <mo>{</mo> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mrow> <mi>O</mi> <mi>l</mi> <mi>r</mi> </mrow> </msub> <mo>(</mo> <mi>k</mi> <mo>)</mo> <mo>-</mo> <msub> <mi>y</mi> <mrow> <mi>O</mi> <mi>l</mi> </mrow> </msub> <mo>(</mo> <mi>k</mi> <mo>)</mo> <mo>)</mo> </mrow> <msub> <mi>w</mi> <mrow> <mi>j</mi> <mi>l</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <msub> <mi>x</mi> <mrow> <mi>H</mi> <mi>O</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>x</mi> <mrow> <mi>H</mi> <mi>O</mi> <mi>j</mi> </mrow> </msub> <mo>(</mo> <mi>k</mi> <mo>)</mo> <mo>)</mo> </mrow> <msub> <mi>x</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>}</mo> <mo>+</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>&amp;beta;</mi> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>(</mo> <mi>k</mi> <mo>)</mo> <mo>-</mo> <msub> <mi>w</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>(</mo> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>
Wherein, η represents learning rate, and β represents factor of momentum；
D8, the weight coefficient w according to formula (6) renewal+1 sampling instant of kthjl(k+1), and k=k+1 is made；
wjl(k+1)=wjl(k)+η(yOlr(k)-yOl(k))xHOj(k)+β(wij(k)-wij(k-1)) (6)
D9, repeat step D2 are to step D8 until meeting k=Tmax, wherein TmaxRepresent maximum sampling number；
D10, according to formula (7) calculate individual ShFitness F (Sh)；
<mrow> <mi>F</mi> <mrow> <mo>(</mo> <msub> <mi>S</mi> <mi>h</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>T</mi> <mi>max</mi> </msub> </munderover> <mi>J</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>M</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> <mo>.</mo> </mrow>
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